Insulating liquids (transformer oils) are dielectrics used in a wide range of electrical equipment and provide a medium for both insulation and cooling. During equipment operation, liquids are subjected to electrical and thermal stresses. With continued use, they chemically degrade and produce degradation products and aging markers. In this study, models based on Fourier-transform infrared spectroscopic (FTIR) measurements of liquids are proposed for estimating insulating liquid properties (acidity, interfacial tension (IFT), and density) using only a single measurement combined with spectral data analysis. Estimation models based on artificial neural networks (ANN) and partial least squares (PLS) were developed through training and validation on approximately 850 samples of mineral insulating liquids. The proposed models provide an effective means for estimating the acidity, IFT, and density of mineral insulating liquids. The models provide estimation results comparable in reproducibility to standardized laboratory analyses, provide the means for a rapid and accurate assessment of the condition of the insulating liquid, as well as allow the design of dedicated sensors to perform these analyses online.

中文翻译：

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基于红外光谱数据的人工神经网络和偏最小二乘回归快速估计矿物绝缘液体特性


绝缘液体（变压器油）是广泛用于电气设备的电介质，并提供绝缘和冷却介质。在设备运行期间，液体会受到电应力和热应力。随着持续使用，它们会发生化学降解并产生降解产物和老化标记。在本研究中，提出了基于液体傅里叶变换红外光谱 (FTIR) 测量的模型，仅使用单一测量结合光谱数据分析来估计绝缘液体特性（酸度、界面张力 (IFT) 和密度）。通过对大约 850 个矿物绝缘液体样本进行训练和验证，开发了基于人工神经网络 (ANN) 和偏最小二乘法 (PLS) 的估计模型。所提出的模型为估计矿物绝缘液体的酸度、IFT 和密度提供了有效的方法。这些模型提供的估计结果在再现性方面可与标准化实验室分析相媲美，提供快速准确评估绝缘液体状况的方法，并允许设计专用传感器来在线执行这些分析。